Accession Number:

ADA021707

Title:

Missing Variables in Bayesian Regression. II,

Descriptive Note:

Corporate Author:

RAND CORP SANTA MONICA CALIF

Personal Author(s):

• Press,S. J.
• Scott,A. J.

Report Date:

1975-06-01

Pagination or Media Count:

13.0

Abstract:

The problem of parameter estimation in normal regression when some of the observations are missing was investigated. A Bayesian approach with vague prior distributions is taken. No assumption is made about the independent variables for which no observations are missing, but the missing components are assumed to be normally distributed with a mean that can depend on the other variables. Joint estimators of the parameters are obtained as the joint mode of the posterior distribution.

Descriptors:

• *VARIABLES
• *BAYES THEOREM
• *LINEAR REGRESSION ANALYSIS
• DATA PROCESSING
• DISTRIBUTION
• PARAMETERS
• OBSERVATION
• ESTIMATES
• VARIATIONS
• REGRESSION ANALYSIS
• LEAST SQUARES METHOD
• VECTOR ANALYSIS

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE